Lucas and Luna scrutinize the messy reality of marketing analytics—where attribution models break, vanity metrics mislead, and campaign data never tells a clean story. Each episode picks a single measurement problem: how last-touch attribution overvalues the final click, why multi-touch models introduce their own biases, or what happens when Facebook and Google report conflicting conversion numbers. Lucas brings the technical rigor—explaining lift studies, incrementality testing, and the statistical pitfalls of small sample sizes—while Luna keeps the conversation tethered to real campaign decisions: budget reallocation, creative testing, and the trade-off between precision and speed. Together they walk through actual brand case studies (from direct-to-consumer startups to enterprise SaaS), showing which metrics mattered, which ones were noise, and how the team eventually reconciled data with strategic judgment. This is not a podcast about marketing automation hacks or growth-hacking gimmicks; it is a podcast for the analyst or manager who stares at a dashboard every morning and needs to know: What is this number actually telling me? And when can I trust it enough to act?

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